





"""simple docstring"""





class 						lowercase   :





      def __init__(			self ,		lowercase					)						->	str:
            lowerCAmelCase    		=			n
            lowerCAmelCase    		=			[None] * self.n
            lowerCAmelCase    		=			0  # index of the first element
            lowerCAmelCase    		=			0
            lowerCAmelCase    		=			0





      def __len__(			self					)						->	int:
            return self.size





      def        _snake_case     (			self					)						->	bool:
            return self.size == 0





      def        _snake_case     (			self					)						->	List[Any]:
            return False if self.is_empty() else self.array[self.front]





      def        _snake_case     (			self ,		lowercase					)						->	List[Any]:
            if self.size >= self.n:
                  raise Exception("""QUEUE IS FULL"""					)

            lowerCAmelCase    		=			data
            lowerCAmelCase    		=			(self.rear + 1) % self.n
            self.size += 1
            return self





      def        _snake_case     (			self					)						->	str:
            if self.size == 0:
                  raise Exception("""UNDERFLOW"""					)

            lowerCAmelCase    		=			self.array[self.front]
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			(self.front + 1) % self.n
            self.size -= 1
            return temp













"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






